Computing resource allocation in three-tier IoT fog networks: a joint optimization approach combining Stackelberg game and matching

Fog computing is a promising architecture to provide economical and low latency data services for future Internet of Things (IoT)-based network systems. Fog computing relies on a set of low-power fog nodes (FNs) that are located close to the end users to offload the services originally targeting at cloud data centers. In this paper, we consider a specific fog computing network consisting of a set of data service operators (DSOs) each of which controls a set of FNs to provide the required data service to a set of data service subscribers (DSSs). How to allocate the limited computing resources of FNs to all the DSSs to achieve an optimal and stable performance is an important problem. Therefore, we propose a joint optimization framework for all FNs, DSOs, and DSSs to achieve the optimal resource allocation schemes in a distributed fashion. In the framework, we first formulate a Stackelberg game to analyze the pricing problem for the DSOs as well as the resource allocation problem for the DSSs. Under the scenarios that the DSOs can know the expected amount of resource purchased by the DSSs, a many-to-many matching game is applied to investigate the pairing problem between DSOs and FNs. Finally, within the same DSO, we apply another layer of many-to-many matching between each of the paired FNs and serving DSSs to solve the FN-DSS pairing problem. Simulation results show that our proposed framework can significantly improve the performance of the IoT-based network systems

Cloud/fog computing resource management and pricing for blockchain networks

The mining process in blockchain requires solving a proof-of-work puzzle, which is resource expensive to implement in mobile devices due to the high computing power and energy needed. In this paper, we, for the first time, consider edge computing as an enabler for mobile blockchain. In particular, we study edge computing resource management and pricing to support mobile blockchain applications in which the mining process of miners can be offloaded to an edge computing service provider. We formulate a two-stage Stackelberg game to jointly maximize the profit of the edge computing service provider and the individual utilities of the miners. In the first stage, the service provider sets the price of edge computing nodes. In the second stage, the miners decide on the service demand to purchase based on the observed prices. We apply the backward induction to analyze the sub-game perfect equilibrium in each stage for both uniform and discriminatory pricing schemes. For the uniform pricing where the same price is applied to all miners, the existence and uniqueness of Stackelberg equilibrium are validated by identifying the best response strategies of the miners. For the discriminatory pricing where the different prices are applied to different miners, the Stackelberg equilibrium is proved to exist and be unique by capitalizing on the Variational Inequality theory. Further, the real experimental results are employed to justify our proposed model.Comment: 16 pages, double-column version, accepted by IEEE Internet of Things Journa

Resource Management for Device-to-Device Communications in Heterogeneous Networks Using Stackelberg Game

Device-to-device (D2D) communications and femtocell systems can bring significant benefits to users’ throughput. However, the complicated three-tier interference among macrocell, femtocell, and D2D systems is a challenging issue in heterogeneous networks. As D2D user equipment (UE) can cause interference to cellular UE, scheduling and allocation of channel resources and power of D2D communication need elaborate coordination. In this paper, we propose a joint scheduling and resource allocation scheme to improve the performance of D2D communication. We take UE rate and UE fairness into account by performing interference management. First, we construct a Stackelberg game framework in which we group a macrocellular UE, a femtocellular UE, and a D2D UE to form a two-leader one-follower pair. The cellular UE are leaders, and D2D UE is the follower who buys channel resources from the leaders. We analyze the equilibrium of the game and obtain solutions to the equilibrium. Second, we propose an algorithm for joint scheduling of D2D pairs based on their utility. Finally, we perform computer simulations to study the performance of the proposed scheme

Distributed resource allocation for data center networks: a hierarchical game approach

The increasing demand of data computing and storage for cloud-based services motivates the development and deployment of large-scale data centers. This paper studies the resource allocation problem for the data center networking system when multiple data center operators (DCOs) simultaneously serve multiple service subscribers (SSs). We formulate a hierarchical game to analyze this system where the DCOs and the SSs are regarded as the leaders and followers, respectively. In the proposed game, each SS selects its serving DCO with preferred price and purchases the optimal amount of resources for the SS's computing requirements. Based on the responses of the SSs' and the other DCOs', the DCOs decide their resource prices so as to receive the highest profit. When the coordination among DCOs is weak, we consider all DCOs are noncooperative with each other, and propose a sub-gradient algorithm for the DCOs to approach a sub-optimal solution of the game. When all DCOs are sufficiently coordinated, we formulate a coalition game among all DCOs and apply Kalai-Smorodinsky bargaining as a resource division approach to achieve high utilities. Both solutions constitute the Stackelberg Equilibrium. The simulation results verify the performance improvement provided by our proposed approaches

Resource Virtualization for Customized Delay-Bounded QoS Provisioning in Uplink VMIMO-SC-FDMA Systems

Wireless Network Virtualization (WNV), which decouples the physical supply process and the service provisioning process, can abstract, isolate and share the physical infrastructure network equipment. This paper studies the resource virtualization in virtual multiple-input multiple-output singlecarrier frequency-division-multiple-access (VMIMO-SC-FDMA) uplink systems, where resources are abstracted to hide the complex details of the fading channel and the link rates are virtualized using the statistical method. Furthermore, the virtual link rates are scheduled and instantiated to different slices with customized delay-bounded quality of service (QoS) provisioning. In this scheme, physical mobile network operator (PMNO) is in charge of the network resource at the physical layer while virtual mobile network operators (VMNOs) are responsible for the traffic admission and the slice management at the MAC layer. Furthermore, we build up the resource virtualization problem as a cross-layer Stackelberg game, which has the interactive dual processes based on the QoS exponent: top-to-down sub-game of leaders at the MAC layer and down-to-top sub-game of follower at the physical layer. Using the newly designed functions for PMNO and VMNOs, we develop an effective dynamic algorithm with iterative dual update to meet the optimization targets of PMNO and VMNOs. Simulation results verify the superiority and stability of delay-bounded QoS guaranteed wireless resource virtualization algorithm developed in this paper in terms of convergence, access rate, and delay-outage probability